Over View Of Meter Data Analytics

Manoj Kumar GuptaNeha consultancy services, Bangalore

manoj4k@gmail.com

Meter Data Analytic

Meter Data Analytics refers to the analysis of data emitted by electric Smart Meters that record consumption of electric energy.

Smart meters send usage data to the central head end systems as often as every minute from each meter whether installed at a residential or a commercial or an industrial customer.

Analytical Methods

Collecting accurate, timely and relevant data is the bedrock of any data analytics program, but the data needs to be put into an appropriate context to become useful information.

1)Aggregations,2)correlations,3) Trending,4)Exception analysis,5) forecasting.

Analytical Methods

Aggregations

An aggregation is a summary of data using set criteria. Because smart meter data is is associated with a metering endpoint, it can be aggregated in dierent ways to serve planning purposes. For instance, the meters connected to individual transformers can be aggregated together to identify transformer loading patterns

Analytical Methods

Correlations

Correlations identify statistical relationships between related data that are useful for building predictions. A basic smart meter correlation is the relationship between outdoor air temperature and power consumption. The fact that heat waves causes spikes in power Consumption is well known fact . Statistical correlation using time-interval consumption data makes it possible to build algorithms that predict the size of demand spikes using forecast temperature.

Analytical Methods

Trending

Trending is one of the most basic forms of analytics, and it can be an obvious win for improving customer relations and service quality using smart meter data. A web page that shows customers a simple consumption data trend line can help them relate power

consumption to household activity.

Analytical methods

Exception Analysis Exceptions are unexpected or abnormal conditions.

A missing meter read, for instance, is an exception event. The ability to analyze exceptions over time is valuable for identifying problems in communications and measurement infrastructure, as well as in the distribution grid. Equipment failure is useful for homing in on a subset of data for other forms of analysis. In the case of a blown transformer, it may be useful to build a historical trend of transformer loading prior to the failure. Once pre-failure patterns are identified, they canbe used to build predictive algorithms useful for preventing future failures.

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Analytical Methods

Forecasts

Forecasts are predictions of future events or values using historical data. For instance, a forecast of power consumption for a new residential subdivision can be created using historical data from similar homes. Forecasts can also be built using correlation data.

Analytics Application

Meters data and analytics will revolutionize the way power is managed, delivered, andso

Applications:Revenue Management,Customer Engagement,Distribution optimization andAMI Network Management.

Challenges of Meter Data Analytics

Data required for complete meter data analytics solution does not reside in the same database, instead, resides in disparate databases among various departments of utility companies.

Meter Data Analytics need to deal with big data problem.

Many utility companies do not have infrastructure to support such needs

Thank you

manoj4k@gmail.com

https://sites.google.com/site/nehaconsultancyservices

Neha Consultancy Services